---
base_model: openai/whisper-medium
datasets:
- google/fleurs
language:
- hi
license: apache-2.0
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: Whisper Medium hindi -megha sharma
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: Google Fleurs
      type: google/fleurs
      config: hi_in
      split: None
      args: 'config: hi, split: test'
    metrics:
    - type: wer
      value: 18.176493557204218
      name: Wer
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# Whisper Medium hindi -megha sharma

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Google Fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3120
- Wer: 18.1765

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- training_steps: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer     |
|:-------------:|:-------:|:----:|:---------------:|:-------:|
| 0.2166        | 0.8475  | 250  | 0.2327          | 26.1128 |
| 0.1217        | 1.6949  | 500  | 0.1955          | 21.5053 |
| 0.0578        | 2.5424  | 750  | 0.2025          | 20.7536 |
| 0.0271        | 3.3898  | 1000 | 0.2230          | 20.5096 |
| 0.0134        | 4.2373  | 1250 | 0.2463          | 20.3046 |
| 0.0105        | 5.0847  | 1500 | 0.2463          | 19.7970 |
| 0.0064        | 5.9322  | 1750 | 0.2636          | 19.2796 |
| 0.0048        | 6.7797  | 2000 | 0.2678          | 19.5920 |
| 0.0034        | 7.6271  | 2250 | 0.2765          | 19.2991 |
| 0.0021        | 8.4746  | 2500 | 0.2710          | 18.5084 |
| 0.0006        | 9.3220  | 2750 | 0.2879          | 19.2015 |
| 0.0001        | 10.1695 | 3000 | 0.2895          | 18.4303 |
| 0.0003        | 11.0169 | 3250 | 0.2930          | 18.3815 |
| 0.0005        | 11.8644 | 3500 | 0.3032          | 18.5963 |
| 0.0001        | 12.7119 | 3750 | 0.3003          | 18.4889 |
| 0.0001        | 13.5593 | 4000 | 0.3054          | 18.4010 |
| 0.0001        | 14.4068 | 4250 | 0.3085          | 18.2058 |
| 0.0           | 15.2542 | 4500 | 0.3104          | 18.1472 |
| 0.0           | 16.1017 | 4750 | 0.3116          | 18.1863 |
| 0.0           | 16.9492 | 5000 | 0.3120          | 18.1765 |


### Framework versions

- Transformers 4.43.3
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1